Expose latent upscale sampler controls

H3_LONG_VIDEOS_GUIDE.md

@@ -1162,13 +1162,14 @@ What this really means:
 - the node renders the beat once
 - the sampled latent is upscaled in latent space to the target size
 - the conditioning is rebuilt at that target size
-- the node then runs a hard-coded 2-step refinement pass over the upscaled latent
+- the node then runs a short refinement pass over the upscaled latent with the sampler, scheduler, step count, denoise, and megapixel target you picked on the latent-upscale params node
 
 Good starting point:
 
 - use the `model` mode when you want the strongest latent detail recovery
 - use the interpolation mode when you want a cheaper resize-only path
-- keep the refinement denoise low to medium
+- start with `euler_ancestral`, `simple`, `2` steps, and `0.2` denoise
+- leave width and height at `0` unless you want an exact override; otherwise `megapixels` drives the target size
 
 The important part is that this stage is still a latent pass, not a pixel-space resize:
 

README.md

@@ -47,9 +47,9 @@
   - Per-shot directives now support `continuity:`, `ref_mode:`, `ref_noise_aug:`, `anchor_add:`, `soundscape:`, and `music:` in addition to the existing timing and wardrobe directives.
 
 - `Dumas H3 Latent Upscale Params`
-  - Inputs: `mode`, `model_name`, `method`, `width`, `height`, `device`, `precision`, `refine_denoise`
+  - Inputs: `mode`, `model_name`, `method`, `width`, `height`, `device`, `precision`, `sampler_name`, `scheduler`, `steps`, `denoise`, `megapixels`
   - Output: `latent_upscale_param`
-  - Bundles the optional latent-space upscaler settings used by `Dumas H3 Long Videos` before decode, so the main node can rebuild conditioning at the target size and run a short refinement pass.
+  - Bundles the optional latent-space upscaler settings used by `Dumas H3 Long Videos` before decode, so the main node can rebuild conditioning at the target size and run a short refinement pass with your chosen sampler, scheduler, step count, and denoise.
 
 - `Dumas H3 Beat Prompt`
   - Inputs: authored through the custom front-end beat editor

dumas_h3_latent_upscale.py

@@ -4,6 +4,7 @@ import math
 import os
 import re
 
+import comfy.samplers
 import folder_paths
 import torch
 
@@ -33,6 +34,8 @@ LATENTS_STD = [
 ]
 
 _LATENT_UPSCALE_FOLDER = "latent_upscale_models"
+MP_UNIT = 1024 * 1024
+RES_MULTIPLE = 32
 
 
 def _models_dir():
@@ -347,10 +350,29 @@ def _compute_upscale_target(width, height, h_in, w_in):
     return h_out, w_out, eff
 
 
+def _scale_to_megapixels(w, h, mp, multiple=RES_MULTIPLE):
+    if not mp or mp <= 0 or w <= 0 or h <= 0:
+        return int(h), int(w)
+    multiple = max(1, int(multiple))
+    scale = math.sqrt((float(mp) * MP_UNIT) / float(w * h))
+    nw = max(multiple, int(round(w * scale / multiple)) * multiple)
+    nh = max(multiple, int(round(h * scale / multiple)) * multiple)
+    return nh, nw
+
+
+def _resolve_target_size(param, h_in, w_in):
+    width = int(param.get("width", 0) or 0)
+    height = int(param.get("height", 0) or 0)
+    megapixels = float(param.get("megapixels", 0.0) or 0.0)
+    if width > 0 and height > 0:
+        return height, width
+    if megapixels > 0:
+        return _scale_to_megapixels(w_in, h_in, megapixels)
+    return int(h_in), int(w_in)
+
+
 def upscale_video_model(video, param):
     model_name = param["model_name"]
-    width = int(param["width"])
-    height = int(param["height"])
     device = param.get("device", "cuda")
     precision = param.get("precision", "fp16")
 
@@ -359,7 +381,8 @@ def upscale_video_model(video, param):
     compute_dtype = {"fp32": torch.float32, "fp16": torch.float16, "bf16": torch.bfloat16}[precision]
 
     _, c, t, h_in, w_in = video.shape
-    h_out, w_out, eff = _compute_upscale_target(width, height, h_in, w_in)
+    h_out, w_out = _resolve_target_size(param, h_in, w_in)
+    eff = (w_out / float(w_in) + h_out / float(h_in)) / 2.0 if w_in and h_in else 1.0
 
     if eff < 1.0 and (w_out < w_in or h_out < h_in):
         raise ValueError("This model only supports upscaling (effective scale >= 1.0).")
@@ -386,10 +409,8 @@ def upscale_video_model(video, param):
 
 def upscale_video_interp(video, param):
     method = str(param.get("method") or "bilinear")
-    width = int(param.get("width", 0) or 0)
-    height = int(param.get("height", 0) or 0)
     _, c, t, h_in, w_in = video.shape
-    h_out, w_out, _ = _compute_upscale_target(width, height, h_in, w_in)
+    h_out, w_out = _resolve_target_size(param, h_in, w_in)
     if h_out == h_in and w_out == w_in:
         return video, h_in, w_in
     video_bt = video.permute(0, 2, 1, 3, 4).reshape(-1, c, h_in, w_in)
@@ -425,23 +446,42 @@ class H3LatentUpscaleParams:
                 "method": (["nearest-exact", "bilinear", "area", "bicubic"], {"default": "bilinear",
                     "tooltip": "Interpolation method used when mode = interp."}),
                 "width": ("INT", {"default": 0, "min": 0, "max": 4096, "step": 32,
-                    "tooltip": "Target upscaled pixel width for the latent refinement stage. 0 keeps the original width and effectively disables the resize."}),
+                    "tooltip": "Explicit target width for the latent refinement stage. Leave at 0 to let megapixels choose the size instead."}),
                 "height": ("INT", {"default": 0, "min": 0, "max": 4096, "step": 32,
-                    "tooltip": "Target upscaled pixel height for the latent refinement stage. 0 keeps the original height and effectively disables the resize."}),
+                    "tooltip": "Explicit target height for the latent refinement stage. Leave at 0 to let megapixels choose the size instead."}),
                 "device": (["cuda", "cpu"], {"default": "cuda",
                     "tooltip": "Device used by the H3 latent upscaler model when mode = model."}),
                 "precision": (["fp16", "fp32", "bf16"], {"default": "fp16",
                     "tooltip": "Computation precision used by the H3 latent upscaler model when mode = model."}),
-                "refine_denoise": ("FLOAT", {"default": 0.25, "min": 0.0, "max": 1.0, "step": 0.01,
+                "sampler_name": (comfy.samplers.KSampler.SAMPLERS, {"default": "euler_ancestral",
-                    "tooltip": "How much the 2-step refinement pass may rewrite the upscaled latent. Lower = safer, higher = freer."}),
+                    "tooltip": "Sampler used for the latent refinement pass. Default matches the current H3 preference."}),
+                "scheduler": (comfy.samplers.KSampler.SCHEDULERS, {"default": "simple",
+                    "tooltip": "Scheduler used for the latent refinement pass."}),
+                "steps": ("INT", {"default": 2, "min": 1, "max": 50, "step": 1,
+                    "tooltip": "Number of refinement steps applied after the latent upscaler stage."}),
+                "denoise": ("FLOAT", {"default": 0.2, "min": 0.0, "max": 1.0, "step": 0.01,
+                    "tooltip": "How much the refinement pass may rewrite the upscaled latent. Lower = safer, higher = freer."}),
+                "megapixels": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 4.0, "step": 0.01,
+                    "tooltip": "Primary target size for the latent refinement stage. If width and height are both set, they win; otherwise the node scales the current shot to this pixel budget while preserving aspect ratio. 0 keeps the incoming latent size."}),
             }
         }
 
-    def build(self, mode, model_name, method, width, height, device, precision, refine_denoise):
+    def build(self, mode, model_name, method, width, height, device, precision, sampler_name, scheduler, steps, denoise, megapixels):
         width = int(width)
         height = int(height)
+        steps = int(steps)
         if mode == "off":
-            return ({"mode": "off", "width": width, "height": height, "refine_denoise": float(refine_denoise)},)
+            return ({
+                "mode": "off",
+                "width": width,
+                "height": height,
+                "sampler_name": sampler_name,
+                "scheduler": scheduler,
+                "steps": steps,
+                "denoise": float(denoise),
+                "refine_denoise": float(denoise),
+                "megapixels": float(megapixels),
+            },)
         if width > 0:
             width = int(round(width / 32.0)) * 32
         if height > 0:
@@ -454,7 +494,12 @@ class H3LatentUpscaleParams:
             "height": height,
             "device": device,
             "precision": precision,
-            "refine_denoise": float(refine_denoise),
+            "sampler_name": sampler_name,
+            "scheduler": scheduler,
+            "steps": steps,
+            "denoise": float(denoise),
+            "refine_denoise": float(denoise),
+            "megapixels": float(megapixels),
         },)
 
 

dumas_h3_longvideos.py

@@ -33,9 +33,9 @@ and your VRAM, chains them, and returns the finished video + audio.
 Requirements: H3 is CFG-free (cfg 1) and needs no negative prompt -- the node
 makes an empty one internally. The main pass keeps denoise fixed at 1.0: a
 partial denoise desyncs the joint audio/video schedule. An optional latent
-upscale stage can rebuild the conditioning at a larger target size, run a short
+upscale stage can rebuild the conditioning at a target size, run a short
-refinement pass, and keep the output video-only before the final pixel-space
+refinement pass with its own sampler controls, and keep the output video-only
-upscale options.
+before the final pixel-space upscale options.
 
 Verified against ComfyUI core (comfy_extras/nodes_minimax_h3.py, model_base.py,
 ldm/minimax/model.py, text_encoders/minimax.py, sd.py).
@@ -4073,6 +4073,17 @@ def _video_only_refined_latent(base_latent, refined_latent):
     return refined_latent
 
 
+def _latent_upscale_target_size(base_w, base_h, param):
+    width = int(param.get("width", 0) or 0)
+    height = int(param.get("height", 0) or 0)
+    if width > 0 and height > 0:
+        return width, height
+    megapixels = float(param.get("megapixels", 0.0) or 0.0)
+    if megapixels > 0:
+        return scale_to_megapixels(base_w, base_h, megapixels)
+    return int(base_w), int(base_h)
+
+
 def _nested_tensor_parts(samples):
     if samples is None:
         return ()
@@ -6107,8 +6118,9 @@ class H3LongVideos:
                                "Try 256 on a tight card at 1344x768."}),
                 "latent_upscale_param": ("DUMAS_H3_LATENT_UPSCALE_PARAM", {
                     "tooltip": "Output of 'Dumas H3 Latent Upscale Params'. When connected, the first-pass "
-                               "latent is upscaled and run through a hard-coded 2-step refinement pass before "
+                               "latent is upscaled and run through a short refinement pass before decode, "
-                               "decode. Leave unconnected to skip latent upscaling entirely."}),
+                               "using the sampler, scheduler, steps, denoise, and megapixel target from that node. "
+                               "Leave unconnected to skip latent upscaling entirely."}),
                 "upscale": (["off", "rtx", "model", "lanczos"], {"default": "off",
                     "tooltip": "Optional post-pass on the finished frames. 'rtx' = NVIDIA RTX Video Super "
                                "Resolution (Tensor Cores -- fastest and best for video; needs the "
@@ -6440,8 +6452,6 @@ class H3LongVideos:
         if (
             isinstance(latent_upscale_param, dict)
             and str(latent_upscale_param.get("mode", "off")) != "off"
-            and int(latent_upscale_param.get("width", 0) or 0) > 0
-            and int(latent_upscale_param.get("height", 0) or 0) > 0
         ):
             try:
                 latent_start = time.perf_counter()
@@ -6463,9 +6473,14 @@ class H3LongVideos:
                     ref_noise_aug=ref_noise_aug, audio_vae=audio_vae, silent=silent)
                 upscale_latent["samples"] = comfy.nested_tensor.NestedTensor(
                     (upscaled_video, parts[1]))
+                refine_steps = int(latent_upscale_param.get("steps", 2) or 2)
+                refine_sampler = latent_upscale_param.get("sampler_name", sn)
+                refine_scheduler = latent_upscale_param.get("scheduler", sch)
+                refine_denoise_value = latent_upscale_param.get("denoise", latent_upscale_param.get("refine_denoise", 0.2))
+                refine_denoise = 0.2 if refine_denoise_value is None else float(refine_denoise_value)
                 (refined_out,) = nodes.common_ksampler(
-                    model, seed, 2, cfg, sn, sch, upscale_cond, negative, upscale_latent,
+                    model, seed, refine_steps, cfg, refine_sampler, refine_scheduler, upscale_cond, negative, upscale_latent,
-                    denoise=float(latent_upscale_param.get("refine_denoise", 0.25) or 0.25))
+                    denoise=refine_denoise)
                 timing["latent_upscale_sample"] += time.perf_counter() - latent_start
                 refined_out = _video_only_refined_latent(out, refined_out)
             except Exception as e:
@@ -6591,18 +6606,20 @@ class H3LongVideos:
             model, ms_note = apply_h3_model_sampling(model, shift_video, shift_audio)
         latent_upscale_note = ""
         if isinstance(latent_upscale_param, dict) and str(latent_upscale_param.get("mode", "off")) != "off":
-            target_w = int(latent_upscale_param.get("width", 0) or 0)
+            target_w, target_h = _latent_upscale_target_size(w, h, latent_upscale_param)
-            target_h = int(latent_upscale_param.get("height", 0) or 0)
-            if target_w > 0 and target_h > 0:
             mode = str(latent_upscale_param.get("mode", "off"))
             detail = f" via {mode}"
             if mode == "model":
                 detail += f"/{latent_upscale_param.get('model_name', 'none')}"
             else:
                 detail += f"/{latent_upscale_param.get('method', 'bilinear')}"
+            denoise_value = latent_upscale_param.get("denoise", latent_upscale_param.get("refine_denoise", 0.2))
+            denoise = 0.2 if denoise_value is None else float(denoise_value)
             latent_upscale_note = (
                 f" latent upscale: target {target_w}x{target_h}px{detail}; "
-                    f"2-step refinement denoise {float(latent_upscale_param.get('refine_denoise', 0.25) or 0.25):.2f}"
+                f"{int(latent_upscale_param.get('steps', 2) or 2)}-step refinement "
+                f"{latent_upscale_param.get('sampler_name', sn)}/{latent_upscale_param.get('scheduler', sch)} "
+                f"denoise {denoise:.2f}"
             )
 
         paras = split_paragraphs(prompt, "##")

tests/test_dumas_h3_longvideos.py

@@ -306,18 +306,21 @@ class DumasH3LongVideosHelperTests(unittest.TestCase):
                 latent_upscale_param={
                     "mode": "model",
                     "model_name": "upscale.safetensors",
-                    "width": 256,
-                    "height": 128,
                     "device": "cpu",
                     "precision": "fp16",
-                    "refine_denoise": 0.4,
+                    "sampler_name": "euler_ancestral",
+                    "scheduler": "simple",
+                    "steps": 2,
+                    "denoise": 0.4,
+                    "megapixels": 1.5,
                 },
             )
 
             self.assertEqual(len(calls), 2)
             self.assertIsNot(calls[1][0][8], first_out)
             self.assertEqual(calls[1][0][8]["samples"].unbind()[0].name, "upv")
-            self.assertEqual(calls[1][0][4], "res_multistep")
+            self.assertEqual(calls[1][0][2], 2)
+            self.assertEqual(calls[1][0][4], "euler_ancestral")
             self.assertEqual(calls[1][0][5], "simple")
             self.assertAlmostEqual(calls[1][1]["denoise"], 0.4)
             self.assertEqual(result[1], first_out)
@@ -424,11 +427,13 @@ class DumasH3LongVideosHelperTests(unittest.TestCase):
                 latent_upscale_param={
                     "mode": "model",
                     "model_name": "upscale.safetensors",
-                    "width": 256,
-                    "height": 128,
                     "device": "cpu",
                     "precision": "fp16",
-                    "refine_denoise": 0.4,
+                    "sampler_name": "euler_ancestral",
+                    "scheduler": "simple",
+                    "steps": 2,
+                    "denoise": 0.4,
+                    "megapixels": 1.5,
                 },
             )
 
@@ -1096,6 +1101,7 @@ class DumasH3LongVideosHelperTests(unittest.TestCase):
 
     def test_latent_upscale_params_node_is_exposed(self):
         latent = importlib.import_module("dumas_h3_latent_upscale")
+        required = latent.H3LatentUpscaleParams.INPUT_TYPES()["required"]
 
         self.assertEqual(
             latent.NODE_CLASS_MAPPINGS,
@@ -1105,6 +1111,11 @@ class DumasH3LongVideosHelperTests(unittest.TestCase):
             latent.NODE_DISPLAY_NAME_MAPPINGS,
             {"DumasH3LatentUpscaleParams": "Dumas H3 Latent Upscale Params"},
         )
+        self.assertEqual(required["sampler_name"][1]["default"], "euler_ancestral")
+        self.assertEqual(required["scheduler"][1]["default"], "simple")
+        self.assertEqual(required["steps"][1]["default"], 2)
+        self.assertEqual(required["denoise"][1]["default"], 0.2)
+        self.assertEqual(required["megapixels"][1]["default"], 1.0)
 
     def test_compose_persistent_does_not_expand_ambiguous_plural_to_full_cast(self):
         active = self.module.parse_wardrobe(